The present invention relates to medical imaging generally and more specifically to a method for acquiring several data acquisition sets during an acquisition period wherein at least one of the acquisitions is a high resolution acquisition and other acquisitions have relatively lesser resolution but are useful for various purposes in addition to generating an image.
Positrons are positively charged electrons which are emitted by radio nuclides that have been prepared using a cyclotron or other device. The radio nuclides most often employed in diagnostic imaging are fluorine-18 (.sup.18 F), carbon-11 (.sup.11 C), nitrogen-13 (.sup.13 N), and oxygen-15 (.sup.15 O). Radio nuclides are employed as radioactive tracers called "radiopharmaceuticals" by incorporating them into substances such as glucose or carbon dioxide. One common use for radiopharmaceuticals is in the medical imaging field.
To use a radiopharmaceutical in imaging, the radiopharmaceutical is injected into a patient and accumulates in an organ, vessel or the like, which is to be imaged. It is known that specific radiopharmaceuticals become concentrated within certain organs or, in the case of a vessel, that specific radiopharmaceuticals will not be absorbed by a vessel wall. The process of concentrating often involves processes such as glucose metabolism, fatty acid metabolism and protein synthesis. Hereinafter, in the interest of simplifying this explanation, an organ to be imaged will be referred to generally as an "organ of interest" and prior art and the invention will be described with respect to a hypothetical organ of interest.
After the radiopharmaceutical becomes concentrated within an organ of interest and while the radio nuclides decay, the radio nuclides emit positrons. The positrons travel a very short distance before they encounter an electron and, when the positron encounters an electron, the positron is annihilated and converted into two photons, or gamma rays. This annihilation event is characterized by two features which are pertinent to medical imaging and particularly to medical imaging using photon emission tomography (PET). First, each gamma ray has an energy of essentially 511 keV upon annihilation. Second, the two gamma rays are directed in substantially opposite directions.
In PET imaging, if the general locations of annihilations can be identified in three dimensions, a three dimensional image of an organ of interest can be reconstructed for observation. To detect annihilation locations, a PET camera is employed. An exemplary PET camera includes a plurality of detectors and a processor which, among other things, includes coincidence detection circuitry. For the purposes of this explanation it will be assumed that a camera includes 12,000 detectors which are arranged to form an annular gantry about an imaging area. Each time a 511 keV photon impacts a detector, the detector generates an electronic signal or pulse which is provided to the processor coincidence circuitry.
The coincidence circuitry identifies essentially simultaneous pulse pairs which correspond to detectors which are essentially on opposite sides of the imaging area. Thus, a simultaneous pulse pair indicates that an annihilation has occurred on a straight line between an associated pair of detectors. Over an acquisition period of a few minutes millions of annihilations are recorded, each annihilation associated with a unique detector pair. After an acquisition period, recorded annihilation data can be used via any of several different well known back projection procedures to construct the three dimensional image of the organ of interest.
To compress annihilation data somewhat, instead of separately storing an indication of each detected annihilation, a typical PET processor simply stores a separate counter for each possible "meaningful" detector pair. What is meant by the term "meaningful" is that there are certain theoretically possible detector pairs which will almost certainly never provide a true annihilation indication. For example, because an annihilation typically sends gamma rays in opposite directions and annihilation points are within an imaging area, it is essentially impossible for two adjacent detectors to provide a true annihilation indication. Similarly, other detectors which are disposed in the same general area as a first detector cannot provide a true annihilation indication along with the first detector. Thus, proximate detectors are not meaningful pairs and the processor does not provide a counter for these pairs.
In addition, the number of meaningful detector pairs is also limited by the fact that certain detector pairs are positioned such that the organ of interest, and any radiopharmaceutical accumulated therein, is not within the space therebetween. In this case, once again, the detector pair cannot provide a true annihilation indication. Thus, while theoretically it is possible to have approximately 144 million detector pairs where there are 12,000 detectors, instead of providing 144 million counters and memory required to store 144 million annihilation counts, the meaningful number of detector pairs and hence processor counters, can be reduced to approximately 25 million. Hereinafter, it will be assumed that memory required to store data corresponding to a single acquisition consists of 25 megabytes (Mb), one byte for each meaningful detector pair.
Given a specific radiopharmaceutical (i.e. a substance characterized by a known positron emission rate), acquisition period duration is primarily dependent upon required image quality. Thus, in cases where poor quality images are acceptable, fewer annihilation events have to be detected to create an image and acquisition period duration can be reduced. However, where high quality images are acceptable, a large number of annihilations must be detected and the acquisition period is typically relatively long. Where an image is to be used for medical diagnostics, usually high quality is extremely important and therefore acquisition periods tend to be relatively long. For the purposes of this explanation it will be assumed that, given a specific radiopharmaceutical, annihilation detections are required over a twenty minute period.
During an acquisition period there are several sources of annihilation detection error. Two of the more prominent sources of detection error are referred to as "dead time" and "randoms". The phenomenon known as dead time occurs when two gamma rays impact a single detector at essentially the same time so that the total absorbed energy far exceeds 511 keV or so that, while a first of the rays is being processed by the detector, a second of the rays is ignored by the detector. In these cases, either one or two annihilations are not recognized and an error occurs.
The phenomenon known as randoms occurs when gamma rays from two different annihilations are detected by two detectors at essentially the same time. For example, assuming two gamma rays are detected at the same time from two different annihilations, the coincidence circuitry cannot determine which detection correspond to a first annihilation and which detection correspond to a second annihilation. The two annihilations that randomly occur at the same time are recorded at a third unrelated location.
Error due to both dead time and randoms should be corrected to provide the highest quality image. To this end, typically, the processor counts dead time errors and randoms and, at the end of an acquisition period and prior to reconstructing an image from the collected data, corrects acquired data. Other common sources of error include attenuation of the gamma rays by different portions of a patient's body and detector gain nonuniformities.
While long acquisition periods (e.g. 20 minutes) are required to generate data needed to reconstruct high resolution and high quality images, long acquisition periods have a number of shortcomings. First, the half life of typical medical grade radiopharmaceuticals is relatively short so that, even during an acquisition period, the rate of gamma ray emissions changes appreciably. The fact that emission rates change over time coupled with errors due to dead time and randoms means that the number of detection errors decreases during the course of an acquisition period. For example, where a detector pair might cause 1000 errors during a first imaging second, the pair may cause only 975 errors during the second imaging second and only 948 errors during a third imaging second and so on. While error correction at the end of a long period does improve image quality, quality can be further improved if errors which occur during shorter time intervals are used to correct data collected during the specific time intervals.
Second, during data acquisition an organ of interest must remain completely still. Any movement of the organ of interest within the imaging area causes data generated before and after the movement to misalign, thus generating a "blur" in a resulting image. Clearly, long acquisition periods increase the likelihood of patient (i.e. organ of interest) movement.
Third, data which is collected over a long period cannot show dynamic occurrences within an organ of interest. For example, often it is desirable to observe how a liquid doped with a radiopharmaceutical travels through a vessel or the rate at which an organ of interest accumulates a radiopharmaceutical, not just the total amount. Unfortunately long acquisition periods do not yield data which has a temporal component. In other words, after acquisition there is no way to distinguish detected annihilations which occurred during a specific acquisition period time interval from annihilations which occurred during other acquisition period time intervals.
Fourth, data which is collected over a long period cannot be displayed in real time during data acquisition. The ability to acquire imaging data over shorter acquisition periods enables the reconstruction and display of a scout image to observe the accumulation of a tracer in real time.
In addition, a system which facilitates observation of dynamic occurrences would be advantageous for determining when radiopharmaceutical concentration within an organ of interest has reached a level required for imaging data acquisition so that a high resolution acquisition period can begin. For example, when a radiopharmaceutical which is to become concentrated in a patient's brain is first injected into the patient, it often takes some time before concentration within the brain occurs. The time required for concentration to occur varies in part on the physiological make up of the patient and therefore, the ideal time for starting an acquisition period varies from patient to patient. In this case a system which facilitates collection of data for generating fast dynamic frames would be useful to detect the bolus of activity as a radiopharmaceutical enters a brain. The bolus of activity can then be used to estimate the best time to start acquiring high resolution data.
To address the problems discussed above, the industry has developed several solutions. For example, in order to generate annihilation detection data which has a temporal component, the acquisition period can be divided into several consecutive time intervals and a separate set of acquisition data for each interval can be generated and stored. Then consecutive data sets can be compared to identify dynamic occurrences and also can be added together to provide data for forming a high resolution image.
Theoretically, consecutive acquisitions could also be used to more accurately correct data for errors due to dead time and randoms. To this end, at the end of each time interval counted errors may be used to correct data acquired during the time interval.
Consecutive acquisitions may also be used to correct data for patient movement. To this end, after an acquisition period, the processor can compare consecutive data to identify movement. When movement occurs, the processor may modify the later acquired data to compensate for the perceived movement. In the alternative, if excessive movement occurred during a single time interval the processor may disregard the blurred data thereby reducing error. At the end of the long acquisition period, the processor may add up the corrected data sets to generate a single data set for constructing an image.
Unfortunately, while the consecutive acquisition solution can be applied in some creative ways to eliminate most of the problems described above, the consecutive acquisition solution has one primary shortcoming, required memory. As indicated above, for every separate data set acquired, the camera system requires 25 Mb of memory. Additional memory is also required to store error data. While 25 Mb of memory is not a problem where only a single data set is acquired, if a separate data set is required each second of an acquisition period, a huge and impractical amount of memory is required. Thus, while this solution can theoretically be employed, this solution is simply to complex and therefore expensive for many applications.
Another solution would be to store only two 25 Mb data sets at any instant during an acquisition and perform correction processes on collected data in real time. For example, during a first second of an acquisition period a first 25 Mb data set could be collected and stored along with detected errors. The first data set could be immediately corrected to compensate for the errors generating a corrected first data set. During a second of the period a second 25 Mb data set could be collected and stored along with detected errors. The second data set could be immediately corrected to compensate for the errors generating a corrected second data set. Then, the corrected second and first data sets could be compared to determine if any movement occurred. Where movement occurred, the second data set could be modified to compensate for the perceived movement generating a modified second set. Thereafter, the corrected first set and modified second set could be added to form a cumulative corrected data set. The cumulative data set could then be stored as the corrected first data set for future comparison to other corrected data sets. Next, a next consecutive 25 Mb data set could be collected along with detected errors and written over the second corrected data set and associated errors in memory. Again, the next consecutive data set would be corrected, compared to the corrected first data set (i.e. the cumulative data set) for movement and modified accordingly if necessary. Thus, all corrections could be performed essentially in real time during acquisition.
While this solution is theoretically possible and has many advantages, this solution is impractical because the computing power required to perform all of the functions described above in real time cannot practically be implemented.
Another solution to correct for patient movement uses a position sensing device such as a camera or a proximity sensor to determine when a patient has moved. To aid in detection an identifiable mark may be placed on the patient's skin which is recognizable by the sensing device. When movement is detected, the detector causes a new annihilation detection acquisition to begin and records data which defines the perceived movement. After an entire acquisition period is completed, the processor compensates the second set of acquired data for the perceived movement and adds the two sets of data together prior to constructing an image.
While this position sensing solution is useful, this solution requires a relatively complex sensing system which can precisely identify three way patient movement. In addition, while this solution can correct for patient movement, this solution does not address the other problems indicated above, namely correction of acquisition errors and facilitating observation of dynamic occurrences.